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20232025
most citedToward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts

8 citations · 14 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CV20258 cited

Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts

Zheng Han, Jun Zhou, Jialun Pei +3

In augmented reality (AR)-guided surgical navigation, preoperative organ models are superimposed onto the patient's intraoperative anatomy to visualize critical structures such as…

cs.CV2024

Robust Noisy Correspondence Learning via Self-Drop and Dual-Weight

Fan Liu, Chenwei Dong, Chuanyi Zhang +2

Many researchers collect data from the internet through crowd-sourcing or web crawling to alleviate the data-hungry challenge associated with cross-modal matching. Although such pr…

cs.CV2024

Hyperspectral Images Efficient Spatial and Spectral non-Linear Model with Bidirectional Feature Learning

Judy X Yang, Jing Wang, Zekun Long +2

Classifying hyperspectral images (HSIs) is a complex task in remote sensing due to the high-dimensional nature and volume of data involved. To address these challenges, we propose…

cs.CV2024

Making Large Vision Language Models to be Good Few-shot Learners

Fan Liu, Wenwen Cai, Jian Huo +3

Few-shot classification (FSC) is a fundamental yet challenging task in computer vision that involves recognizing novel classes from limited data. While previous methods have focuse…

cs.CV20242 cited

Unsupervised Band Selection Using Fused HSI and LiDAR Attention Integrating With Autoencoder

Judy X Yang, Jun Zhou, Jing Wang +2

Band selection in hyperspectral imaging (HSI) is critical for optimising data processing and enhancing analytical accuracy. Traditional approaches have predominantly concentrated o…

cs.CV20241 cited

Enhancing Zero-shot Counting via Language-guided Exemplar Learning

Mingjie Wang, Jun Zhou, Yong Dai +2

Recently, Class-Agnostic Counting (CAC) problem has garnered increasing attention owing to its intriguing generality and superior efficiency compared to Category-Specific Counting…